WO2001033547B1 - Methods and apparatuses for signal analysis - Google Patents
Methods and apparatuses for signal analysisInfo
- Publication number
- WO2001033547B1 WO2001033547B1 PCT/NL2000/000808 NL0000808W WO0133547B1 WO 2001033547 B1 WO2001033547 B1 WO 2001033547B1 NL 0000808 W NL0000808 W NL 0000808W WO 0133547 B1 WO0133547 B1 WO 0133547B1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- signal
- segment
- input
- time
- correlator
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract 48
- 210000000721 basilar membrane Anatomy 0.000 claims abstract 56
- 238000001914 filtration Methods 0.000 claims abstract 14
- 238000013144 data compression Methods 0.000 claims abstract 2
- 230000005284 excitation Effects 0.000 claims 15
- 230000004044 response Effects 0.000 claims 9
- 230000005236 sound signal Effects 0.000 claims 9
- 230000001427 coherent effect Effects 0.000 claims 5
- 238000010367 cloning Methods 0.000 claims 4
- 230000001419 dependent effect Effects 0.000 claims 4
- 230000006870 function Effects 0.000 claims 4
- 230000000873 masking effect Effects 0.000 claims 4
- 238000001228 spectrum Methods 0.000 claims 4
- 238000004590 computer program Methods 0.000 claims 3
- 239000011159 matrix material Substances 0.000 claims 2
- 206010001497 Agitation Diseases 0.000 claims 1
- 239000000203 mixture Substances 0.000 claims 1
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/90—Pitch determination of speech signals
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
- G01R23/175—Spectrum analysis; Fourier analysis by delay means, e.g. tapped delay lines
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Signal Processing (AREA)
- Computational Linguistics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Quality & Reliability (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
- Complex Calculations (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
A basilar membrane model is used to receive an input signal including a target signal in step I. With successive further steps the target signal is filtered from the input signal. After the filtering the target signal can be used as an input for further processing, like for example signal recognition of data compression. The target signal can also be applied to a substantially reverse method to obtain an improved or clean signal.
Claims
1. An apparatus for estimating frequency characteristics of an input signal, including: a basilar membrane model device to which the signal is applied and a correlator device connected to the basilar membrane model device, said correlator device having: a first input connected to a segment of the basilar membrane model, for receiving a BM (basilar membrane) signal which stems from the segment, which BM signal is present for a predetermined period of time; and at least one second input connected to the same segment of the basilar membrane model device, for receiving the BM signal shifted over an adjustable time shift Tl, and which correlator device provides a time shift Tl_dependent output signal which is further dependent on frequencies substantially present in the BM signal of the segment and which output signal forms a measure for the frequency content of the signal.
2. An apparatus as claimed in claim 1, wherein said adjustable time shift Tl is adjusted to correspond substantially to the inverse of the characteristic frequency of the segment.
»
3. « An apparatus as claimed in claim 1 or 2, wherein a multiple of segments of the basilar membrane model are each connected to a separate correlator device , and wherein the apparatus further includes: a cross-correlator device connected to the respective outputs of at least a number of the correlator devices for determining common periods predominantly present in the BM signals of the segments.
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4. An apparatus as claimed in any one of the preceding claims, wherein said correlator device is implemented as a leaky autocorrelator device arranged for performing an operation represented by the mathematical algorithm:
, wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift and L is a low-pass filtering method.
5. An apparatus as claimed in any one of the preceding claims, wherein said correlator device is implemented as a low pass filter device arranged for performing an operation represented by the mathematical algorithm:
, wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift and L is a low-pass filtering method
6. An apparatus as claimed in any one of claims 1-3, wherein said correlator device is implemented as a time normalised correlator device arranged for performing an operation represented by the mathematical algorithm:
R;,T(t) = L(xt(t),x,(t + T))
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, herein R+ is the output signal, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift and L is a low -pass filtering method.
7. An apparatus as claimed in any one of the preceding claims, wherein said correlator device is implemented as a time normalised correlator device with a group delay corrector,
8. An apparatus as claimed in claim 7, wherein said time normal- ised correlator device with a group delay corrector is arranged for performing an operation represented by the mathematical algorithm:
R%.(t) = (xs(t + dI),x!(t + ds + T))
, wherein Red is the output signal, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, L is the group delay as a function of the segment position and L is a low-pass filtering method.
9. An apparatus for determining the spectrum of an source signal including: a basilar membrane model device to which an input signal including the source signal is applied; and a correlator device connected to the basilar membrane model, which correlator device includes: a first input for receiving a BM signal which stems from a segment of the basilar membrane, for each of a multiple of segments of the basilar mem- brane; and at least one second input for receiving the BM signal shifted over an adjustable time shift T2, and which correlator device provides a segment-dependent output signal which forms a measure for an energy spectrum predominantly present in the source signal.
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10. An apparatus as claimed in claim 9 and any one of claims 3-8 , wherein the adjustable time T2 is set to depend on at least one common period predominantly present in the BM signals of the segments.
11. An apparatus as claimed in claim 10, wherein said adjustable time T2 is set to further depend on a segment dependent group delay of BM signals.
12. A apparatus for determining peaks in a signal including: a basilar membrane model device to which the signal is applied; a multiple of integrator devices, each integrator device being connected with an input to a segment of the basilar membrane model and for generating a excitation signal from the BM signal and transmitting the excitation signal to an output of the integrator device, a 3 dimensional matrix of the excitation signal by segment position by time forming a cochleogram ; a peak search device connected to the outputs of the integrator devices, for determining peaks in the excitation signals; in which apparatus a correlator device is connected to segments of the basi- lar membrane model and which correlator device is also communicatively connected to the peak search device, and to which correlator device the excitation signal of a segment of a selected position is applied, which selected position and correspondingly the segment varies in time depending on peaks determined by the peak search device.
13. An apparatus as claimed in claim 12, wherein said correlator device is implemented as a leaky autocorrelator device arranged for performing an operation represented by the mathematical algorithm:
R.( ,r( = £(*.ω ( . ,(,)(t - 0)
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, wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, and L is a low-pass filtering method.
14. An apparatus as claimed in any one of claims 12-13, wherein said correlator device is implemented as a time normalised correlator device arranged for performing an operation represented by the mathematical algorithm:
R;(rχτ(t) = L(xi(r)(t),xa(l)(t + T))
, wherein R+ is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, and L is a low-pass filtering method.
15. An apparatus as claimed in any one of claims 12-14, wherein said correlator device is implemented as a time normalised correlator with a group delay corrector
16. An apparatus as claimed in claim 14, wherein said time normalised correlator with a group delay corrector is arranged for performing an operation represented by the mathematical algorithm:
>.r( = x^it +d^x^ +d, + D)
, wherein RsA is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, dβ is the group delay as a function of the segment position and L is a low-pass filtering method.
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17. An apparatus as claimed in any one of the preceding claims, further including: a fpce device for fundamental period contour estimation, connected to the basilar membrane model device, the fpce device including: an input connected to the basilar membrane model; a ridge determination device, for determining estimated ridges and instan- taneous period contours; a ridge selector device connected to the ridge determination device, for selecting the most reliable smooth instantaneous period contours; an harmonic cloning device connected to the ridge selector device, for cloning of period contours to all possible fundamental periods, each combination of period contours of possible fundamental periods forming a contour hypothesis; a cloned contour selector device connected to the harmonic cloning device for selecting the most reliable period contours; a selector device connected to the cloned contour selector device for selecting at least one contour hypothesis that corresponds to a substantial part of the selected most reliable smooth instantaneous period contours; and an output connected to the selector device for transmitting the selected contour hypothesis further.
18. An apparatus as claimed in any one of the preceding claims, wherein said signal is a sound signal.
19. A signal recognition system including: an input; an apparatus as claimed in any one of claims 1-18;
memory means connected to the device, the memory means containing data representing signals to be recognised; a processor device arranged for comparing a signal from the output of said apparatus with the signals to be recognised and determining a most similar signal most similar to the signal from the output of said apparatus from the signals to be recognised; an output.
20. A signal recognition system as claimed in claim 19, wherein said signals to be recognised represent speech signals.
21. A data compression syste including: an input; an apparatus as claimed in any of claims 1-18 connected to the input; processor means for reading the signal component values determined by said device of a signal received at the input of the device and transmitting the values to an output connected to the processor means.
22. A data expansion system including; an input for receiving signal component values determined with a system as claimed in claim 21, processsor means for reading the signal component values and reconstructing an original signal; an output for outputting the original signal.
23. A signal improvement system including: an input; a first apparatus as claimed in any one of claims 1-18;
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a masking device for selecting parts of a BM signal, the masking device being connected to an output of the first device; a second apparatus being substantially an inverse of the first apparatus for reconstructing a cochleogram of the selected parts of the BM signal, and having an input connected to the output of the masking device; an output connected to an output of the second device.
24. A signal improvement system as claimed in claim 22, wherein said masking device includes a coherent ridge estimation device for select- ing coherent ridges; a sine response adder device for replacing the selected coherent ridges with a sine response; an adder device for replacing a sine response with an original signal if the intensity of the sine response is lower than the intensity of the original sig- nal; a smoother device for removing discontinuities in the signal.
25. A method for estimating frequency characteristics of a source signal including the steps of: receiving said source signal at an input; simulating a response to said source signal of a basilar membrane having a number of segments, whereby an input signal is generated; generating at least one excitation signal of a basilar membrane segment from said input signal; whereby the 3 dimensional matrix of the excitation signal by time by segment forms a cochleogram; generating a shifted signal by shifting at least one of the at least one excitation signals with an adjustable time shift; combining at least one of the at least one excitation signals with said shifted signal, whereby a measure of the correlation between said excitation signal and the at least one of the at least one shifted signals is obtained.
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26. A method as claimed in claim 25, wherein said shifted signal is generated from an input signal and the shifted signal is combined with that same input signal.
27. A method as claimed in claim 26, wherein said shifted signal is generated from an input signal and the shifted signal is combined with that same input signal for each of a multiple of basilar membrane segments.
28. A method as claimed in any one of the claims 25-27, wherein said combining is performed by a leaky autocorrelation step including performing an operation represented by the mathematical algorithm:
_. = !, ..-- '„max
, wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift and L is a low-pass filtering method.
29. A method as claimed in any one of the claims 25-27, wherein said combining is performed by a time normalised correlation step including per- forming an operation represented by the mathematical algorithm:
R r( = L(x,(t).x,(t + T
, herein R* is the output signal, X is the BM signal, s is the segment posi- tion, t is the time, T is the adjustable time shift and is a low-pass filtering method.
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30. A method as claimed in any one of the claims 25-29, wherein said combining is performed by a time normalised correlation step including a group delay correction step.
31. A method as claimed in claim 30, wherein said combining is performed by a time normalised correlation step including performing an operation represented by the mathematical algorithm:
R (t) = L(xI(t + dt)txt(t + da + T))
, wherein Red is the output signal, X is the BM signal, s is the segment posi- tion, t is the time, T is the adjustable time shift, α is the group delay as a function of the segment position and L is a low-pass filtering method.
32. A method as claimed in any one of claims 25-31 , wherein the adjustable time T2 is set to depend on at least one common period predomi- nantly present in the BM signals of the segments.
33. A method as claimed in claim 32, whereby after the step of generating at least one excitation signal, a step of determining a time by segment region of said cochleogram comprising a frequency component of said excita- tion signal is performed, and said time shift is adjusted depending on the determined region.
34. A method for determining peaks in a signal including: receiving said source signal at an input; simulating a response to said source signal of a basilar membrane having a number of segments, whereby an input signal is generated; generating at least one excitation signal of a basilar membrane segment from said input signal; determining peaks in the excitation signals;
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combining an excitation signal of a segment of a selected position, which selected position and correspondingly the segment varies in time depending on peaks determined.
35. A method as claimed in claim 34, wherein said combining is performed by a leaky autocorrelation step including performing an operation represented by the mathematical algorithm:
R.(,,.r( = (*,(,, (O. ' - ^>) , wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, and L is a low-pass filtering method.
36. A method as claimed in claim 34, wherein said combining is performed by a time normalised correlation step including performing an op- eration represented by the mathematical algorithm:
nAi) = χ 3U (t),χ i(l)(t + T))
, wherein R^ is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, and L is a low-pass filtering method,
37. A method as claimed in any one of claims 33-35, wherein said combining is implemented as a time normalised correlation step with a group delay correction
38. A method as claimed in claim 37, wherein said time normalised correlation with a group delay correction includes performing an operation represented by the mathematical algorithm:
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* r( = Ux,ι, it + df ),xi l)(t + J. + E))
, wherein ed is the output signal of the correlator device, X is the BM sig- nal, s is the segment position, t is the time, T is the adjustable time shift, d_ is the group delay as a function of the segment position and L is a low-pass filtering method.
39. A method as claimed in claims 25-37, further including: estimating a fundamental period contour estimation including: determining estimated ridges and instantaneous period contours; selecting the most reliable smooth instantaneous period contours; cloning of period contours to all possible fundamental periods, each combination of period contours of possible fundamental periods forming a contour hypothesis; selecting the most reliable period contours; selecting at least one contour hypothesis that corresponds to a substantial part of the selected most reliable smooth instantaneous period contours transmitting the selected contour hypothesis further.
40. A method as claimed in claims 25-38, wherein said signal is a sound signal.
41. A method for recognising signals including: a method as claimed in any one of claims 25-39; comparing an output signal with a signals to be recognised and determining from the signals to be recognised a most similar signal most similar to the signal.
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42. A method as claimed in claim 40, wherein said signals to be recognised represent speech signals.
43. A method for compressing data including: a method as claimed in any one of claims 25-43; reading the signal component values determined with said method of a signal received and transmitting the values further.
44. A method for expanding data including: receiving signal component values determined with a method as claimed in claim 44, reading the signal component values and reconstructing an original signal; outputting the original signal.
45. A method for improving a signal including: a first method as claimed in any one of claims 25-39; selecting parts of a BM signal; a second method substantially an inverse of the first method for reconstructing a cochleogram of the selected parts of the BM signal, using as an input the selected parts.
46. A method as claimed in claim 45, wherein said selecting includes selecting coherent ridges; replacing the selected coherent ridges with a sine response; replacing a sine response with an original signal if the intensity of the sine response is lower than the intensity of the original signal; removing discontinuities in the signal.
47. A method as claimed in any one of claims 25-46 wherein said sound signal contains speech from at least one speaker,
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48. A method as claimed in any one of claims 25-47, wherein said source signal is a unknown mixture of signals
49. A method as claimed in any one of claims 24-48, wherein after the step of selecting said detected signal, said detected signal is analysed further.
50. A computer program for running on a computer system, charac- terised in that the computer program contains code portions for performing steps of the methods as claimed in any one of claims 25-49 when ran on a computer system.
51. A data carrier containing data representing a computer program as is claimed in claim 50,
52 A device for estimating the frequency content of a sound signal which exhibits noise, comprising a basilar membrane model arranged for receiving the sound signal, which basilar membrane model comprises a number of series-connected segments, and a low -pass filter connected to the basilar membrane, which provides an estimated signal, characterised in that the low-pass filter is designed as a multiplier having a first and a second input, while to the first input is arranged for receiving a signal which stems from a segment of the basilar membrane and is present for a prede- termined period of time, and to the second input is arranged for receiving the signal shifted over an adjustable time Tl, and in that the multiplier provides a tune Tl_dependent output signal which depends on the -frequency substantially present in the signal of that segment and forms a measure for the frequency content of the sound signal.
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53. A device for estimating the spectrum of a sound signal which exhibits noise, comprising a basilar membrane model arranged for receiving the sound signal, which model comprises a number of series-connected segments, and a low-pass filter connected to the basilar membrane, which pro- vides an estimated signal, characterised in that the low-pass filter is designed as a multiplier having a first and a second input, wherein in use for each segment of the basilar membrane, to the first input a signal is applied which stems from that segment and to the second input said signal is applied shifted over a time T2, and that the multiplier provides a segment- dependent output signal which forms a measure for a firequency energy spectrum substantially present in the sound signal.
Priority Applications (6)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US10/129,460 US6745155B1 (en) | 1999-11-05 | 2000-11-06 | Methods and apparatuses for signal analysis |
| CA2390244A CA2390244C (en) | 1999-11-05 | 2000-11-06 | Methods and apparatuses for signal analysis |
| DE60033549T DE60033549T2 (en) | 1999-11-05 | 2000-11-06 | METHOD AND DEVICE FOR SIGNAL ANALYSIS |
| EP00980108A EP1228502B1 (en) | 1999-11-05 | 2000-11-06 | Methods and apparatuses for signal analysis |
| JP2001535156A JP4566493B2 (en) | 1999-11-05 | 2000-11-06 | Signal analysis method and apparatus |
| AU17408/01A AU1740801A (en) | 1999-11-05 | 2000-11-06 | Methods and apparatuses for signal analysis |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| NL1013500A NL1013500C2 (en) | 1999-11-05 | 1999-11-05 | Apparatus for estimating the frequency content or spectrum of a sound signal in a noisy environment. |
| NL1013500 | 1999-11-05 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| WO2001033547A1 WO2001033547A1 (en) | 2001-05-10 |
| WO2001033547B1 true WO2001033547B1 (en) | 2001-11-29 |
Family
ID=19770203
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/NL2000/000808 WO2001033547A1 (en) | 1999-11-05 | 2000-11-06 | Methods and apparatuses for signal analysis |
Country Status (10)
| Country | Link |
|---|---|
| US (1) | US6745155B1 (en) |
| EP (1) | EP1228502B1 (en) |
| JP (1) | JP4566493B2 (en) |
| CN (1) | CN1286084C (en) |
| AT (1) | ATE354849T1 (en) |
| AU (1) | AU1740801A (en) |
| CA (1) | CA2390244C (en) |
| DE (1) | DE60033549T2 (en) |
| NL (1) | NL1013500C2 (en) |
| WO (1) | WO2001033547A1 (en) |
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| US5473759A (en) * | 1993-02-22 | 1995-12-05 | Apple Computer, Inc. | Sound analysis and resynthesis using correlograms |
| US6072885A (en) * | 1994-07-08 | 2000-06-06 | Sonic Innovations, Inc. | Hearing aid device incorporating signal processing techniques |
| EP0852708B1 (en) * | 1995-09-29 | 2001-09-12 | International Business Machines Corporation | Mechanical signal processor based on micromechanical oscillators and intelligent acoustic detectors and systems based thereon |
| US5856722A (en) * | 1996-01-02 | 1999-01-05 | Cornell Research Foundation, Inc. | Microelectromechanics-based frequency signature sensor |
| US5879283A (en) * | 1996-08-07 | 1999-03-09 | St. Croix Medical, Inc. | Implantable hearing system having multiple transducers |
| US6501399B1 (en) * | 1997-07-02 | 2002-12-31 | Eldon Byrd | System for creating and amplifying three dimensional sound employing phase distribution and duty cycle modulation of a high frequency digital signal |
| EP0980064A1 (en) * | 1998-06-26 | 2000-02-16 | Ascom AG | Method for carrying an automatic judgement of the transmission quality of audio signals |
-
1999
- 1999-11-05 NL NL1013500A patent/NL1013500C2/en not_active IP Right Cessation
-
2000
- 2000-11-06 EP EP00980108A patent/EP1228502B1/en not_active Expired - Lifetime
- 2000-11-06 AT AT00980108T patent/ATE354849T1/en not_active IP Right Cessation
- 2000-11-06 AU AU17408/01A patent/AU1740801A/en not_active Abandoned
- 2000-11-06 JP JP2001535156A patent/JP4566493B2/en not_active Expired - Fee Related
- 2000-11-06 CA CA2390244A patent/CA2390244C/en not_active Expired - Fee Related
- 2000-11-06 CN CN00818227.2A patent/CN1286084C/en not_active Expired - Fee Related
- 2000-11-06 DE DE60033549T patent/DE60033549T2/en not_active Expired - Lifetime
- 2000-11-06 US US10/129,460 patent/US6745155B1/en not_active Expired - Lifetime
- 2000-11-06 WO PCT/NL2000/000808 patent/WO2001033547A1/en active Search and Examination
Also Published As
| Publication number | Publication date |
|---|---|
| CN1286084C (en) | 2006-11-22 |
| JP4566493B2 (en) | 2010-10-20 |
| ATE354849T1 (en) | 2007-03-15 |
| AU1740801A (en) | 2001-05-14 |
| EP1228502A1 (en) | 2002-08-07 |
| WO2001033547A1 (en) | 2001-05-10 |
| CA2390244C (en) | 2011-07-19 |
| CA2390244A1 (en) | 2001-05-10 |
| DE60033549D1 (en) | 2007-04-05 |
| CN1421030A (en) | 2003-05-28 |
| JP2003513339A (en) | 2003-04-08 |
| DE60033549T2 (en) | 2007-11-22 |
| EP1228502B1 (en) | 2007-02-21 |
| US6745155B1 (en) | 2004-06-01 |
| NL1013500C2 (en) | 2001-05-08 |
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